Spaces:
Running
Running
File size: 47,845 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"期待値: 225.0\n",
"標準偏差: 75.0\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import binom\n",
"\n",
"# パラメータ設定\n",
"n = 10 # 試行回数\n",
"p = 0.5 # 成功確率\n",
"damage = 50\n",
"\n",
"# x軸の値（0 から n までの整数）\n",
"x = np.arange(0, n+1)\n",
"\n",
"# 二項分布の確率質量関数 (PMF)\n",
"y = binom.pmf(x, n, p)\n",
"\n",
"# プロット\n",
"x_label = [str(i * damage) for i in x]\n",
"plt.xticks(x, x_label)\n",
"\n",
"plt.bar(x, y)\n",
"plt.xlabel('Damage')\n",
"plt.ylabel('Probability')\n",
"\n",
"evalue = n * p\n",
"std = np.sqrt(n * p * (1 - p))\n",
"print('期待値: ', evalue * damage)\n",
"print('標準偏差: ', std * damage)\n",
"# グラフ表示\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1σ内: 0.8203124999999998\n",
"1σ内のダメージ: [150 200 250 300]\n"
]
}
],
"source": [
"index_within_1std = np.where(np.abs(x - evalue) <= std)\n",
"print('1σ内: ', np.sum(y[index_within_1std]))\n",
"damages = x[index_within_1std] * damage\n",
"print('1σ内のダメージ: ', damages)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"max_n = 11\n",
"\n",
"evalues = []\n",
"stds = []\n",
"for i in range(1, max_n + 1):\n",
" evalues.append(i * p * damage)\n",
" stds.append(np.sqrt(i * p * (1 - p)) * damage)\n",
"\n",
"plus_stds = [evalues[i] + stds[i] for i in range(max_n)]\n",
"minus_stds = [evalues[i] - stds[i] for i in range(max_n)]\n",
"max_damages = [i * damage for i in range(1, max_n + 1)]\n",
"\n",
"# plt.bar(range(1, max_n + 1), evalues, label='Expected value', color='gray')\n",
"\n",
"\n",
"plt.bar(range(1, max_n + 1), max_damages, label='Max damage')\n",
"plt.bar(range(1, max_n + 1), plus_stds, label='Expected value + Standard deviation', linestyle='dashed')\n",
"plt.bar(range(1, max_n + 1), evalues, label='Expected value')\n",
"plt.bar(range(1, max_n + 1), minus_stds, label='Expected value - Standard deviation', linestyle='dashed', color='gray')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.xlabel('Number of energy')\n",
"plt.ylabel('Damage')\n",
"plt.show()\n",
"requeirments\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|